Traditional pathological samples have been largely processed using methods that involve killing the cells or lengthy sample processing times. Such methods are generally performed in a laboratory well away from the point of care. These traditional methods do not permit the examination of live cells, including dynamic, live-cell related biomarkers, and do not allow for rapid sample processing or analytical result generation at the point of care. This lack of complete and rapidly obtained information can prevent doctors from identifying the proper treatment regimen or at the least slow the process which adversely effects the patient's quality of life. A comparison of the traditional process to some improved embodiments is shown in FIG. 9.
For example, oncologists have a number of treatment options available to them, including different combinations of drugs that are characterized as standard of care, and a number of drugs that do not carry a label claim for a particular cancer, but for which there is evidence of efficacy in that cancer. The best likelihood of good treatment outcome requires that patients be assigned to optimal available cancer treatment, and that this assignment be made as quickly as possible following diagnosis.
While some cancers can be readily identified using genomic markers, reliable genomic markers are not available for all cancers, which may be better characterized as exhibiting abnormal expression of one or (typically) many normal genes. Currently available diagnostic tests to diagnose particular types of cancer and evaluate the likely effectiveness of different treatment strategies based on gene expression may have one or more disadvantages, for example: (1) the tests may be designed for testing blood and are not readily adapted for testing solid tumors; (2) sample preparation methods for solid tumor samples, including disaggregation of cells, may be unsuitable for handling live cells or performing subsequent measurements of marker expression; (3) small samples, e.g., obtained using fine needle biopsies, may not provide sufficient tissue for complete analysis; (4) the tests may require in vitro culturing of the cells, extended incubation periods, and/or significant delays between the time that the test cells are obtained from the patient and the time the cells are tested, resulting potential for wide variation and external influences on marker expression; (5) the tests may be unsuited for measuring expression of a multiplicity of genes, phosphoproteins or other markers in parallel, which may be critical for recognizing and characterizing the expression as abnormal; (6) the tests may be non-quantitative, relying principally on immunohistochemistry to determine the presence or absence of a protein as opposed to relative levels of expression of genes; (7) the reagents and cell handling conditions are not strictly controlled, leading to a high degree of variability from test to test and lab to lab; (8) the tests may be unsuited to analyzing RNA levels, due to the instability of RNA and the practical difficulty of obtaining sufficiently fresh samples from the patients; and (9) the tests may involve fixing of the cells before any gene expression analysis can be performed, e.g., in the presence or absence of selected reagents.
Recently, several groups have published studies concerning the classification of various cancer types by microarray gene expression analysis (see, e.g. Golub et al., Science 286:531-537 (1999); Bhattacharjae et al., Proc. Nat. Acad. Sci. USA 98:13790-13795 (2001); Chen-Hsiang et al., Bioinformatics 17 (Suppl. 1): S316-S322 (2001); Ramaswamy et al., Proc. Natl. Acad. Sci. USA 98:1514915154 (2001)). Certain classifications of human breast cancers based on gene expression patterns have also been reported (Martin et al., Cancer Res. 60:2232-2238 (2000); West et al., Proc. Natl. Acad. Sci. USA 98:11462-11467 (2001); Sorlie et al., Proc. Natl. Acad. Sci. USA 98:1086910874 (2001); Yan et al., Cancer Res. 61:8375-8380 (2001)). However, these studies mostly focus on improving and refining the already established classification of various types of cancer, including breast cancer, and generally do not provide new insights into the relationships of the differentially expressed genes. These studies do not link the findings to treatment strategies in order to improve the clinical outcome of cancer therapy, and they do not address the problem of improving and standardizing existing techniques of cell handling and analysis.
Although modern molecular biology and biochemistry have revealed more than 100 genes whose activities influence the behavior of tumor cells, state of their differentiation, and their sensitivity or resistance to certain therapeutic drugs, with a few exceptions, the status of these genes has not been exploited for the purpose of routinely making clinical decisions about drug treatments. One notable exception is the use of estrogen receptor (ER) protein expression in breast carcinomas to select patients to treatment with anti-estrogen drugs, such as tamoxifen. Another exceptional example is the use of ErbB2 (Her2) protein expression in breast carcinomas to select patients with the Her2 antagonist drug Herceptin® (Genentech, Inc., South San Francisco, Calif.). For most cancers, however, the pathologies in gene expression may be subtler and may involve patterns of expression of multiple genes or expression of genes in response to particular stimuli.
The challenge of cancer treatment remains to target specific treatment regimens to pathogenically distinct tumor types, and to identify the optimal treatment as early as possible in order to optimize outcome. Hence, a need exists for tests that simultaneously provide prognostic and/or predictive information about patient responses to the variety of treatment options.
There is a need for a device and a method to prepare solid tumor biopsies or otherwise aggregated cells which address these disadvantages and integrate, in a single small and compact apparatus, the function of handling and preparing tissue samples using controlled, consistent and efficient steps; maintaining viability of the tissue sample, to permit stimulation and/or preservation of different biomarker responses from the same tissue sample before the sample loses viability or becomes cultured through ex vivo replication.